QoS-constrained List Scheduling Heuristics for Parallel Applications on Grids

Abstract The MATLAB is a technical computing language used in a variety of fields, such as control systems, image and signal processing, visualization, financial process simulations in an easy-to-use environment. MATLAB offers "toolboxes" which are specialized libraries for variety scientific domains, and a simplified interface to high-performance libraries (LAPACK, BLAS, FFTW too). Now MATLAB is enriched by the possibility of parallel computing with the Parallel Computing ToolboxTM and MATLAB Distributed Computing ServerTM. In this article we present some of the key features of MATLAB parallel applications focused on using GPU processors for image processing.

Download Full-text

List Scheduling Algorithm for Static Task with Precedence Constraints for Cyber-physical Systems

ACTA AUTOMATICA SINICA ◽

10.3724/sp.j.1004.2012.01870 ◽

2012 ◽

Vol 38 (11) ◽

pp. 1870 ◽

Cited By ~ 8

Author(s):

Xiao-Le WANG ◽

Hong-Bin HUANG ◽

Su DENG

Keyword(s):

Scheduling Algorithm ◽

Cyber Physical Systems ◽

Precedence Constraints ◽

List Scheduling ◽

Physical Systems

Download Full-text

Performance Analysis of List Scheduling Algorithms by Random Synthetic DAGs

SSRN Electronic Journal ◽

10.2139/ssrn.3349016 ◽

2019 ◽

Cited By ~ 2

Author(s):

Wakar Ahmad ◽

Bashir Alam ◽

Sahil Malik

Keyword(s):

Performance Analysis ◽

Scheduling Algorithms ◽

List Scheduling ◽

List Scheduling Algorithms

Download Full-text

Statistical and machine learning models for optimizing energy in parallel applications

The International Journal of High Performance Computing Applications ◽

10.1177/1094342019842915 ◽

2019 ◽

Vol 33 (6) ◽

pp. 1079-1097 ◽

Cited By ~ 2

Author(s):

Mark Endrei ◽

Chao Jin ◽

Minh Ngoc Dinh ◽

David Abramson ◽

Heidi Poxon ◽

...

Keyword(s):

Machine Learning ◽

Energy Efficiency ◽

High Performance ◽

Large Scale ◽

Energy Use ◽

Parallel Applications ◽

Learning Models ◽

Trade Off ◽

Time Required ◽

Machine Learning Models

Rising power costs and constraints are driving a growing focus on the energy efficiency of high performance computing systems. The unique characteristics of a particular system and workload and their effect on performance and energy efficiency are typically difficult for application users to assess and to control. Settings for optimum performance and energy efficiency can also diverge, so we need to identify trade-off options that guide a suitable balance between energy use and performance. We present statistical and machine learning models that only require a small number of runs to make accurate Pareto-optimal trade-off predictions using parameters that users can control. We study model training and validation using several parallel kernels and more complex workloads, including Algebraic Multigrid (AMG), Large-scale Atomic Molecular Massively Parallel Simulator, and Livermore Unstructured Lagrangian Explicit Shock Hydrodynamics. We demonstrate that we can train the models using as few as 12 runs, with prediction error of less than 10%. Our AMG results identify trade-off options that provide up to 45% improvement in energy efficiency for around 10% performance loss. We reduce the sample measurement time required for AMG by 90%, from 13 h to 74 min.

Download Full-text